Method and device for processing a time-dependent measurement signal

ABSTRACT

A monitoring device is arranged to receive a time-dependent measurement signal from a pressure sensor in a fluid containing system, which is associated with a first pulse generator and a second pulse generator. The pressure sensor is arranged in the fluid containing system to detect a first pulse originating from the first pulse generator and a second pulse originating from the second pulse generator. The monitoring device is configured to process the measurement signal to remove the first pulse. In this process, the monitoring device receives the measurement signal, obtains a first pulse profile which is a predicted temporal signal profile of the first pulse, and filters the measurement signal in the time-domain, using the first pulse profile, to essentially eliminate the first pulse while retaining the second pulse. The fluid containing system may include an extracorporeal blood flow circuit and a blood circuit of a human patient.

TECHNICAL FIELD

The present invention generally relates to processing of time-dependentmeasurement signals obtained from a fluid containing system, and inparticular to filtering such a measurement signal for removal ofpressure pulses originating from a specific pulse generator. The presentinvention is e.g. applicable in fluid containing systems forextracorporeal blood treatment.

BACKGROUND ART

In extracorporeal blood treatment, blood is taken out of a patient,treated and then reintroduced into the patient by means of anextracorporeal blood flow circuit. Generally, the blood is circulatedthrough the circuit by one or more pumping devices. The circuit isconnected to a blood vessel access of the patient, typically via one ormore access devices, such as needles or catheters, which are insertedinto the blood vessel access. Such extracorporeal blood treatmentsinclude hemodialysis, hemodiafiltration, hemofiltration, plasmapheresis,etc.

US2005/0010118 proposes a technique for monitoring a patient's pulserate, blood pressure and also the condition of the blood vessel access,by identifying a frequency component of the pressure wave caused by thepatent's heartbeat among other pressure waves in the extracorporealblood flow circuit, by operating a frequency analysis, such as a Fouriertransformation, on a pressure signal obtained from a pressure sensor inthe extracorporeal blood flow circuit. As noted in US2005/0010118, itmight be difficult to extract the relevant frequency component from amixture of frequency components caused by mechanical devices in theextracorporeal blood flow circuit and by the heartbeat. In particular,the frequency component of the heartbeat may overlap with a frequencycomponent of the mechanical devices. To overcome this limitation,US2005/0010118 proposes e.g. changing the frequency of the blood pumpwithin a certain range of a basic operating frequency during thetreatment procedure. The pressure signal from the pressure sensor in theextracorporeal blood flow circuit is analysed by FFT (Fast FourierTransform), which is not suited for detection of frequency componentswhose frequencies are constantly changing. The FFT analysis is allegedto reduce the frequency components caused by the blood pump. However,periodic events caused by other mechanical devices in the extracorporealblood flow circuit, such as valves, may still interfere with themonitoring. Further, it may be undesirable to operate the blood pumpwith a constantly changing pumping frequency during the treatmentprocedure. For example, if the extracorporeal blood flow circuit is partof a dialysis machine, the dialysis dose will decline with changedpumping frequency even at unchanged average flow through theextracorporeal blood flow circuit.

Thus, there is a need for an alternative technique for identifying thepatent's heartbeat among other pressure waves in a fluid, and inparticular a technique with an improved ability to handle the situationwhen the frequency of the patient's heartbeat is relatively weak and/orat least partially coincides with a frequency component of these otherpressure waves and/or is changing over time.

Corresponding needs may arise in other fields of technology. Thus,generally speaking, there is a need for an improved technique forprocessing a time-dependent measurement signal obtained from a pressuresensor in a fluid containing system associated with a first pulsegenerator and a second pulse generator, in order to monitor a functionalparameter of the fluid containing system by isolating a signal componentoriginating from the second pulse generator among signal componentsoriginating from the first and second pulse generator.

SUMMARY

It is an object of the invention to at least partly fulfil one or moreof the above-identified needs in view of the prior art.

This and other objects, which will appear from the description below,are at least partly achieved by means of a method, a control device, anda computer program product according to the independent claims,embodiments thereof being defined by the dependent claims.

A first aspect of the invention is a method for processing atime-dependent measurement signal obtained from a pressure sensor in afluid containing system associated with a first pulse generator and asecond pulse generator, wherein the pressure sensor is arranged in thefluid containing system to detect a first pulse originating from thefirst pulse generator and a second pulse originating from the secondpulse generator, said method comprising: receiving the measurementsignal; obtaining a first pulse profile which is a predicted temporalsignal profile of the first pulse; and filtering the measurement signalin the time-domain, using the first pulse profile, to essentiallyeliminate the first pulse while retaining the second pulse.

In one embodiment, the step of filtering comprises subtracting the firstpulse profile from the measurement signal, wherein the step ofsubtracting may comprise adjusting a phase of the first pulse profile inrelation to the measurement signal, wherein said phase may be indicatedby phase information obtained from a phase sensor coupled to the firstpulse generator, or from a control unit for the first pulse generator.

In one embodiment, the first pulse profile is obtained in a referencemeasurement in said fluid containing system, wherein the referencemeasurement comprises the steps of: operating the first pulse generatorto generate at least one first pulse, and obtaining the first pulseprofile from a reference signal generated by a reference pressure sensorin the fluid containing system. The first pulse generator may beoperated to generate a sequence of first pulses during the referencemeasurement, and the first pulse profile may be obtained by identifyingand averaging a set of first pulse segments in the reference signal.Alternatively or additionally, the reference measurement may be effectedintermittently during operation of the fluid containing system toprovide an updated first pulse profile. Alternatively or additionally,the pressure sensor may be used as said reference pressure sensor.Alternatively or additionally, the fluid containing system may beoperated, during the reference measurement, such that the referencesignal contains a first pulse and no second pulse. Alternatively, thereference measurement comprises: obtaining a combined pulse profilebased on a first reference signal containing a first pulse and a secondpulse; obtaining a second pulse profile based on a second referencesignal containing a second pulse and no first pulse, and obtaining thepredicted signal profile by subtracting the second pulse profile fromthe combined pulse profile.

In one embodiment, the step of obtaining comprises obtaining apredetermined signal profile, wherein the step of obtaining may furthercomprise modifying the predetermined signal profile according to amathematical model based on a current value of one or more systemparameters of the fluid containing system.

In one embodiment, the method further comprises the step of obtaining acurrent value of one or more system parameters of the fluid containingsystem, wherein the first pulse profile is obtained as a function of thecurrent value.

In one embodiment, the step of obtaining the first pulse profilecomprises: identifying, based on the current value, one or morereference profiles in a reference database; and obtaining the firstpulse profile based on said one or more reference profiles. The systemparameter(s) may be indicative of the rate of first pulses in the fluidcontaining system. The first pulse generator may comprise a pumpingdevice and the system parameter may be indicative of a pump frequency ofthe pumping device. Each reference profile in the reference database maybe obtained by a reference measurement in the fluid containing systemfor a respective value of said one or more system parameters.

In one embodiment, the step of obtaining the first pulse profilecomprises: identifying, based on the current value, one or morecombinations of energy and phase angle data in a reference database; andobtaining the first pulse profile based on said one or more combinationsof energy and phase angle data. The first pulse profile may be obtainedby combining a set of sinusoids of different frequencies, wherein theamplitude and phase angle of each sinousoid may be given by said one ormore combinations of energy and phase angle data.

In one embodiment, the step of obtaining the first pulse profilecomprises: inputting the current value into an algorithm whichcalculates the response of the pressure sensor based on a mathematicalmodel of the fluid containing system.

In one embodiment, the step of filtering comprises subtracting the firstpulse profile from the measurement signal, and the step of subtractingis preceded by an adjustment step, in which at least one of theamplitude, the time scale and the phase of the first pulse profile isadjusted with respect to the measurement signal. The adjustment step maycomprise minimizing a difference between the first pulse profile and themeasurement signal.

In one embodiment, the step of filtering comprises: supplying the firstpulse profile as input to an adaptive filter; calculating an errorsignal between the measurement signal and an output signal of theadaptive filter; and providing the error signal as input to the adaptivefilter, whereby the adaptive filter is arranged to essentially eliminatethe first pulse in the error signal. The adaptive filter may comprise afinite impulse response filter with filter coefficients that operate onthe first pulse profile to generate the output signal, and an adaptivealgorithm which optimizes the filter coefficients as a function of theerror signal and the first pulse profile. Alternatively or additionally,the method may further comprise the step of controlling the adaptivefilter to lock the filter coefficients, based on a comparison of therate and/or amplitude of the second pulses to a limit value.

In one embodiment, the fluid containing system comprises anextracorporeal blood flow circuit for connection to a blood system in ahuman body, and wherein the first pulse generator comprises a pumpingdevice in the extracorporeal blood flow circuit, and wherein the secondpulse generator comprises a physiological pulse generator in the humanbody. The second pulse generator may be at least one of a heart, abreathing system, and a vasomotor affected by an autonomic nervoussystem. In one implementation, the extracorporeal blood flow circuitcomprises an arterial access device, a blood processing device, and avenous access device, wherein the human blood system comprises a bloodvessel access, wherein the arterial access device is configured to beconnected to the human blood system, wherein the venous access device isconfigured to be connected to the blood vessel access to form a fluidconnection, and wherein the first pulse generator comprises a pumpingdevice arranged in the extracorporeal blood flow circuit to pump bloodfrom the arterial access device through the blood processing device tothe venous access device, said method comprising the step of receivingthe measurement signal either from a venous pressure sensor locateddownstream of the pumping device, or from an arterial pressure sensorlocated upstream of the pumping device.

A second aspect of the invention is a computer program productcomprising instructions for causing a computer to perform the methodaccording to the first aspect.

A third aspect of the invention is a device for processing atime-dependent measurement signal obtained from a pressure sensor in afluid containing system associated with a first pulse generator and asecond pulse generator, wherein the pressure sensor is arranged in thefluid containing system to detect a first pulse originating from thefirst pulse generator and a second pulse originating from the secondpulse generator, said device comprising: an input for the measurementsignal; a signal processor connected to said input and comprising aprocessing module configured to obtain a first pulse profile which is apredicted temporal signal profile of the first pulse, and to filter themeasurement signal in the time-domain, using the first pulse profile, toessentially eliminate the first pulse while retaining the second pulse.

A fourth aspect of the invention is a device for processing atime-dependent measurement signal obtained from a pressure sensor in afluid containing system associated with a first pulse generator and asecond pulse generator, wherein the pressure sensor is arranged in thefluid containing system to detect a first pulse originating from thefirst pulse generator and a second pulse originating from the secondpulse generator, said device comprising: means for receiving themeasurement signal; means for obtaining a first pulse profile which is apredicted temporal signal profile of the first pulse; and means forfiltering the measurement signal in the time-domain, using the firstpulse profile, to essentially eliminate the first pulse while retainingthe second pulse.

A fifth aspect is a method for processing a time-dependent measurementsignal obtained from a pressure sensor in a fluid containing systemassociated with a first pulse generator and a second pulse generator,wherein the pressure sensor is arranged in the fluid containing systemto detect a first pulse originating from the first pulse generator and asecond pulse originating from the second pulse generator, said methodcomprising: receiving the measurement signal; obtaining a standardsignal profile of the first pulse; and subtracting the standard signalprofile from the measurement signal in the time-domain, wherein thestandard signal profile has such an amplitude and phase that the firstpulse is essentially eliminated and the second pulse is retained.

A sixth aspect is a device for processing a time-dependent measurementsignal obtained from a pressure sensor in a fluid containing systemassociated with a first pulse generator and a second pulse generator,wherein the pressure sensor is arranged in the fluid containing systemto detect a first pulse originating from the first pulse generator and asecond pulse originating from the second pulse generator, said devicecomprising: an input for the measurement signal; a signal processorconnected to said input and comprising a processing module configured toobtain a standard signal profile of the first pulse, and to subtract thestandard signal profile from the measurement signal in the time-domain,wherein the standard signal profile has such an amplitude and phase thatthe first pulse is essentially eliminated and the second pulse isretained.

Embodiments of the third to sixth aspects may correspond to theabove-identified embodiments of the first aspect.

Still other objectives, features, aspects and advantages of the presentinvention will appear from the following detailed description, from theattached claims as well as from the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplifying embodiments of the invention will now be described in moredetail with reference to the accompanying schematic drawings.

FIG. 1 is a schematic view of a general fluid containing system in whichthe inventive data processing may be used for filtering a pressuresignal.

FIG. 2 is a flow chart of a monitoring process according to anembodiment of the invention.

FIG. 3( a) is a plot of a pressure signal as a function of time, andFIG. 3( b) is a plot of the pressure signal after filtering.

FIG. 4 is a schematic view of a system for hemodialysis treatmentincluding an extracorporeal blood flow circuit.

FIG. 5( a) is a plot in the time domain of a venous pressure signalcontaining both pump frequency components and a heart signal, and FIG.5( b) is a plot of the corresponding signal in the frequency domain.

FIG. 6 is a plot of a predicted signal profile originating from aperistaltic pump in the system of FIG. 4.

FIG. 7 is a flow chart of a process for obtaining the predicted signalprofile.

FIG. 8 is a plot to illustrate an extrapolation process for generatingthe predicted signal profile.

FIG. 9( a) is a plot to illustrate an interpolation process forgenerating the predicted signal profile, and FIG. 9( b) is an enlargedview of FIG. 9( a).

FIG. 10( a) represents a frequency spectrum of a pressure pulseoriginating from a pumping device at one flow rate, FIG. 10( b)represents corresponding frequency spectra for three different flowrates, wherein each frequency spectrum is given in logarithmic scale andmapped to harmonic numbers, FIG. 10( c) is a plot of the data in FIG.10( b) in linear scale, and FIG. 10( d) is a phase angle spectrumcorresponding to the frequency spectrum in FIG. 10( a).

FIG. 11 is schematic view of an adaptive filter structure operable tofilter a measurement signal based on a predicted signal profile.

FIG. 12( a) illustrates a filtered pressure signal (top) and acorresponding heart signal (bottom), obtained from a venous pressuresensor, and FIG. 12( b) illustrates a filtered pressure signal (top) anda corresponding heart signal (bottom), obtained from an arterialpressure sensor.

DETAILED DESCRIPTION OF EXEMPLIFYING EMBODIMENTS

In the following, exemplifying embodiments of the invention will bedescribed with reference to fluid containing systems in general.Thereafter, the embodiments and implementations of the invention will befurther exemplified in the context of systems for extracorporeal bloodtreatment.

Throughout the following description, like elements are designated bythe same reference signs.

General

FIG. 1 illustrates a fluid containing system in which a fluid connectionC is established between a first fluid containing sub-system S1 and asecond fluid containing sub-system S2. The fluid connection C may or maynot transfer fluid from one sub-system to the other. A first pulsegenerator 3 is arranged to generate a series of pressure waves in thefluid within the first sub-system S1, and a second pulse generator 3′ isarranged to generate a series of pressure waves in the fluid within thesecond sub-system S2. A pressure sensor 4 a is arranged to measure thefluid pressure in the first sub-system S1. Pressure waves generated bythe second pulse generator 3′ will travel from the second sub-system S2to the first sub-system S1, via the connection C, and thus second pulsesoriginating from the second pulse generator 3′ will be detected by thepressure sensor 4 a in addition to first pulses originating from thefirst pulse generator 3. It is to be noted that either one of the firstand second pulse generators 3, 3′ may include more than onepulse-generating device. Further, any such pulse-generating device mayor may not be part of the respective sub-system S1, S2.

The system of FIG. 1 further includes a surveillance device 25 which isconnected to the pressure sensor 4 a, and possibly to one or moreadditional pressure sensors 4 b, 4 c, as indicated in FIG. 1. Thereby,the surveillance device 25 acquires one or more pressure signals thatare time-dependent to provide a real time representation of the fluidpressure in the first sub-system S1.

Generally, the surveillance device 25 is configured to monitor afunctional state or functional parameter of the fluid containing system,by isolating and analysing one or more second pulses in one of thepressure signals. As will be further exemplified in the following, thefunctional state or parameter may be monitored to identify a faultcondition, e.g. in the first or second sub-systems S1, S2, the secondpulse generator 3′ or the fluid connection C. Upon identification of afault condition, the surveillance device 25 may issue an alarm orwarning signal and/or alert a control system of the first or secondsub-systems S1, S2 to take appropriate action. Alternatively oradditionally, the surveillance device 25 may be configured to record oroutput a time sequence of values of the functional state or parameter.

Depending on implementation, the surveillance device 25 may use digitalcomponents or analog components, or a combination thereof, for receivingand processing the pressure signal. The device 25 may thus be acomputer, or a similar data processing device, with adequate hardwarefor acquiring and processing the pressure signal in accordance withdifferent embodiments of the invention. Embodiments of the invention maye.g. be implemented by software instructions that are supplied on acomputer-readable medium for execution by a processor 25 a inconjunction with a memory unit 25 b in the computer.

Typically, the surveillance device 25 is configured to continuouslyprocess the time-dependent pressure signal(s) to isolate any secondpulses. This processing is schematically depicted in the flow chart ofFIG. 2. The illustrated processing involves a step 201 of obtaining afirst pulse profile u(n) which is a predicted temporal signal profile ofthe first pulse(s), and a step 202 of filtering the pressure signald(n), or a pre-processed version thereof, in the time-domain, using thefirst pulse profile u(n), to essentially eliminate or cancel the firstpulse(s) while retaining the second pulse(s) contained in d(n). In thecontext of the present disclosure, n indicates a sample number and isthus equivalent to a (relative) time point in a time-dependent signal.In step 203, the resulting filtered signal e(n) is then analysed for thepurpose of monitoring the aforesaid functional state or parameter.

The first pulse profile is a shape template or standard signal profile,typically given as a time-sequence of data values, which reflects theshape of the first pulse in the time domain. The first pulse profile isalso denoted “predicted signal profile” in the following description.

By “essentially eliminating” is meant that the first pulse(s) is(are)removed from the pressure signal to such an extent that the secondpulse(s) can be detected and analysed for the purpose of monitoring theaforesaid functional state or parameter.

By filtering the pressure signal in the time-domain, using the firstpulse profile, it is possible to essentially eliminate the first pulsesand still retain the second pulses, even if the first and second pulsesoverlap or nearly overlap in the frequency domain. Such a frequencyoverlap is not unlikely, e.g. if one or both of the first and secondpulses is made up of a combination of frequencies or frequency ranges.

Furthermore, the frequency, amplitude and phase content of the firstpulse or the second pulse may vary over time. Such variations may be theresult of an active control of the first and/or second pulse generator3, 3′, or be caused by drifts in the first and/or second pulse generator3, 3′ or by changes in the hydrodynamic properties of the sub-systemsS1, S2 or the fluid connection C. Frequency variations may occur, e.g.,when the second pulse generator 3′ is a human heart, and the secondsub-system S2 thus is the blood system of a human. In healthy subjectsunder calm conditions, variations in heart rhythm (heart ratevariability, HRV) may be as large as 15%. Unhealthy subjects may sufferfrom severe heart conditions such as atrial fibrillation andsupraventricular ectopic beating, which may lead to an HRV in excess of20%, and ventricular ectopic beating, for which HRV may be in excess of60%. These heart conditions are not uncommon among, e.g., dialysispatients.

Any frequency overlap may make it impossible or at least difficult toisolate the second pulses in the pressure signal by conventionalfiltering in the frequency domain, e.g. by operating a comb filterand/or a combination of band-stop or notch filters, typically cascadecoupled, on the pressure signal to block out all frequency componentsoriginating from the first pulse generator 3. Furthermore, frequencyvariations make it even harder to successfully isolate second pulses inthe pressure signal, since the frequency overlap may vary over time.Even in the absence of any frequency overlap, frequency variations makeit difficult to define filters in the frequency domain.

Depending on how well the first pulse profile represents the firstpulse(s) in the pressure signal, it may be possible to isolate thesecond pulses by means of the inventive filtering in the time-domaineven if the first and second pulses overlap in frequency, and even ifthe second pulses are much smaller in amplitude than the first pulses.

Still further, the inventive filtering in the time domain may allow fora faster isolation of second pulses in the pressure signal than afiltering process in the frequency domain. The former may have theability to isolate a single second pulse in the pressure signal whereasthe latter may need to operate on a sequence of first and second pulsesin the pressure signal. Thus, the inventive filtering may enable fasterdetermination of the functional state or functional parameter of thefluid containing system.

The effectiveness of the inventive filtering is exemplified in FIG. 3,in which FIG. 3( a) shows an example of a time-dependent pressure signald(n) containing first and second pulses with a relative magnitude of10:1. The first and second pulses have a frequency of 1 Hz and 1.33 Hz,respectively. Due to the difference in magnitude, the pressure signal isdominated by the first pulses. FIG. 3( b) shows the time-dependentfiltered signal e(n) that is obtained after applying the inventivefiltering technique to the pressure signal d(n). The filtered signale(n) is made up of second pulses and noise. It should be noted thatthere is an absence of second pulses after about 4 seconds, which may beobserved by the surveillance device (25 in FIG. 1) and identified as afault condition of the fluid containing system.

Reverting to FIG. 2, the inventive data processing comprises two mainsteps: a determination of the first pulse profile u(n) (step 201) and aremoval of one or more first pulses from a measurement signal d(n) usingthe first pulse profile u(n) (step 202).

There are many ways to implement these main steps. For example, thefirst pulse profile (standard signal profile) may be obtained in areference measurement, based on a measurement signal from one or more ofthe pressure sensors 4 a-4 c in the first sub-system S1, suitably byidentifying and possibly averaging a set of first pulse segments in themeasurement signal(s). The first pulse profile may or may not be updatedintermittently during the actual monitoring of the aforesaid functionalstate or parameter. Alternatively, a predetermined (i.e. predefined)standard signal profile may be used, which optionally may be modifiedaccording to a mathematical model accounting for wear in the first pulsegenerator, fluid flow rates, tubing dimensions, speed of sound in thefluid, etc. Further, the removal may involve subtracting the first pulseprofile from the measurement signal at suitable amplitude and phase. Thephase may be indicated by phase information which may be obtained from asignal generated by a phase sensor coupled to the first pulse generator3, or from a control signal for the first pulse generator 3.

The inventive filtering may also be combined with other filteringtechniques to further improve the quality of the filtered signal e(n).In one embodiment, the filtered signal e(n) could be passed through abandpass filter with a passband in the relevant frequency range for thesecond pulses. If the second pulses originate from a human heart, thepassband may be located within the approximate range of 0.5-4 Hz,corresponding to heart pulse rates of 30-240 beats per minute. Inanother embodiment, if the current frequency range (or ranges) of thesecond pulses is known, the passband of the bandpass filter could beactively controlled to a narrow range around the current frequencyrange. For example, such an active control may be applied whenever therates of first and second pulses are found to differ by more than acertain limit, e.g. about 10%. The current frequency range may beobtained from the pressure signal, either by intermittently shutting offthe first pulse generator 3, or intermittently preventing the firstpulses from reaching the relevant pressure sensor 4 a-4 c.Alternatively, the current frequency range may be obtained from adedicated sensor in either the first or the second sub-systems S1, S2,or based on a control unit (not shown) for the second pulse generator3′. According to yet another alternative, the location and/or width ofthe passband could be set, at least in part, based on patient-specificinformation, i.e. existing data records for the patient, e.g. obtainedin earlier treatments of the same patient. The patient-specificinformation may be stored in an internal memory of the surveillancedevice (25 in FIG. 1), on an external memory which is made accessible tothe surveillance device, or on a patient card where the information ise.g. transmitted wirelessly to the surveillance device, e.g. by RFID(Radio Frequency IDentification).

These and other embodiments will be explained in further detail below,within the context of a system for extracorporeal blood treatment. Tofacilitate the following discussion, details of an exemplifyingextracorporeal blood flow circuit will be first described.

Monitoring in an Extracorporeal Blood Flow Circuit

FIG. 4 shows an example of an extracorporeal blood flow circuit 20 ofthe type which is used for dialysis. The extracorporeal blood flowcircuit 20 (also denoted “extracorporeal circuit”) comprises components1-14 to be described in the following. Thus, the extracorporeal circuit20 comprises an access device for blood extraction in the form of anarterial needle 1, and an arterial tube segment 2 which connects thearterial needle 1 to a blood pump 3 which may be of peristaltic type, asindicated in FIG. 4. At the inlet of the pump there is a pressure sensor4 b (hereafter referred to as “arterial sensor”) which measures thepressure before the pump in the arterial tube segment 2. The blood pump3 forces the blood, via a tube segment 5, to the blood-side of adialyser 6. Many dialysis machines are additionally provided with apressure sensor 4 c (hereafter referred to as “system sensor”) thatmeasures the pressure between the blood pump 3 and the dialyser 6. Theblood is lead via a tube segment 10 from the blood-side of the dialyser6 to a venous drip chamber or deaeration chamber 11 and from there backto the patient via a venous tube segment 12 and an access device forblood reintroduction in the form of a venous needle 14. A pressuresensor 4 a (hereafter referred to as “venous sensor”) is provided tomeasure the pressure on the venous side of the dialyser 6. In theillustrated example, the pressure sensor 4 a measures the pressure inthe venous drip chamber. Both the arterial needle 1 and the venousneedle 14 are connected to the patient by means of a blood vesselaccess. The blood vessel access may be of any suitable type, e.g. afistula, a Scribner-shunt, a graft, etc. Depending on the type of bloodvessel access, other types of access devices may be used instead ofneedles, e.g. catheters. The access devices 1, 14 may alternatively becombined into a single unit.

In relation to the fluid containing system in FIG. 1, the extracorporealcircuit 20 corresponds to the first sub-system S1, the blood pump 3 (aswell as any further pulse source(s) within or associated with theextracorporeal circuit 20, such as a dialysis solution pump, valves,etc) corresponds to the first pulse generator 3, the blood system of thepatient corresponds to the second sub-system S2, and the fluidconnection C corresponds to at least one of the venous-side andarterial-side fluid connections between the patient and theextracorporeal circuit 20.

In FIG. 4, a control unit 23 is provided, i.a., to control the bloodflow in the extracorporeal circuit 20 by controlling the revolutionspeed of the blood pump 3. The extracorporeal circuit 20 and the controlunit 23 may form part of an apparatus for extracorporeal bloodtreatment, such as a dialysis machine. Although not shown or discussedfurther it is to be understood that such an apparatus performs manyother functions, e.g. controlling the flow of dialysis fluid,controlling the temperature and composition of the dialysis fluid, etc.

The system in FIG. 4 also includes a surveillance/monitoring device 25,which is connected to receive a pressure signal from at least one of thepressure sensors 4 a-4 c and which executes the inventive dataprocessing. In the example of FIG. 4, the surveillance device 25 is alsoconnected to the control unit 23. Alternatively or additionally, thedevice 25 may be connected to a pump sensor 26 for indicating therevolution speed and/or phase of the blood pump 3. It is to beunderstood that the surveillance device 25 may include inputs forfurther data, e.g. any other system parameters that represent theoverall system state (see e.g. discussion with reference to FIG. 7below). The device 25 is tethered or wirelessly connected to a local orremote device 27 for generating an audible/visual/tactile alarm orwarning signal. Alternatively or additionally, either device 25, 27 mayinclude a display or monitor for displaying the functional state orparameter resulting from the analysis step (203 in FIG. 2), and/or thefiltered signal e(n) resulting from the filtering step (202 in FIG. 2),e.g. for visual inspection.

In FIG. 4, the surveillance device 25 comprises a data acquisition part28 for pre-processing the incoming signal(s), e.g. including an A/Dconverter with a required minimum sampling rate and resolution, one ormore signal amplifiers, and one or more filters to remove undesiredcomponents of the incoming signal(s), such as offset, high frequencynoise and supply voltage disturbances.

After the pre-processing in the data acquisition part 28, thepre-processed pressure signal is provided as input to a main dataprocessing part 29, which executes the inventive data processing. FIG.5( a) shows an example of such a pre-processed pressure signal in thetime domain, and FIG. 5( b) shows the corresponding power spectrum, i.e.the pre-processed pressure signal in the frequency domain. The powerspectrum reveals that the detected pressure signal contains a number ofdifferent frequency components emanating from the blood pump 3. In theillustrated example, there is a frequency component at the basefrequency (f₀) of the blood pump (at 1.5 Hz in this example), as well asits harmonics 2f₀, 3f₀ and 4f₀. The base frequency, also denoted pumpfrequency in the following, is the frequency of the pump strokes thatgenerate pressure waves in the extracorporeal circuit 20. For example,in a peristaltic pump of the type shown in FIG. 4, two pump strokes aregenerated for each full revolution of the rotor 3 a. FIG. 5( b) alsoindicates the presence of a frequency component at half the pumpfrequency (0.5f₀) and harmonics thereof, in this example at least f₀,1.5f₀, 2f₀ and 2.5f₀. FIG. 5( b) also shows a heart signal (at 1.1 Hz)which in this example is approximately 40 times weaker than the bloodpump signal at the base frequency f₀.

The main data processing part 29 executes the aforesaid steps 201-203.In step 202, the main data processing part 29 operates to filter thepre-processed pressure signal in the time domain, and outputs a filteredsignal or monitoring signal (e(n) in FIG. 2) in which the signalcomponents of the blood pump 3 have been removed. The monitoring signalstill contains any signal components that originate from the patient(cf. FIG. 3( b)), such as pressure pulses caused by the beating of thepatient's heart. There are a number of sources to cyclic physiologicalphenomena that may generate pressure pulses in the blood stream of thepatient, including the heart, the breathing system, or the vasomotor,which is controlled by the autonomic nervous system. Thus, themonitoring signal may contain pressure pulses resulting from acombination of cyclic phenomena in the patient. Generally speaking, thesignal components in the monitoring signal may originate from any typeof physiological phenomenon in the patient, or combinations thereof, beit cyclic or non-cyclic, repetitive or non-repetitive, autonomous ornon-autonomous.

Depending on implementation, the surveillance device 25 may beconfigured apply further filtering to the monitoring signal to isolatesignal components originating from a single cyclic phenomenon in thepatient. Alternatively, such signal component filtering is done duringthe pre-processing of the pressure signal (by the data acquisition part28). The signal component filtering may be done in the frequency domain,e.g. by applying a cut-off or bandpass filter, since the signalcomponents of the different cyclic phenomena in the patient aretypically separated in the frequency domain. Generally, the heartfrequency is about 0.5-4 Hz, the breathing frequency is about 0.15-0.4Hz, the frequency of the autonomous system for regulation of bloodpressure is about 0.04-0.14 Hz, the frequency of the autonomous systemfor regulation of body temperature is about 0.04 Hz.

The surveillance device 25 could be configured to monitor the breathingpattern of the patient, by identifying breathing pulses in themonitoring signal. The resulting information could be used for on-linesurveillance for apnoea, hyperventilation, hypoventilation, asthmaticattacks or other irregular breathing behaviours of the patient. Theresulting information could also be used to identify coughing, sneezing,vomiting or seizures. The vibrations resulting fromcoughing/sneezing/vomiting/seizures might disturb other measurement orsurveillance equipment that is connected to the patient or theextracorporeal circuit 20. The surveillance device 25 may be arranged tooutput information about the timing of anycoughing/sneezing/vomiting/seizures, such that other measurement orsurveillance equipment can take adequate measures to reduce thelikelihood that the coughing/sneezing/vomiting/seizures results inerroneous measurements or false alarms. Of course, the ability ofidentifying coughing/sneezing/vomiting/seizures may also have a medicalinterest of its own.

The surveillance device 25 could be configured to monitor the heart rateof the patient, by identifying heart pulses in the monitoring signal.

The surveillance device 25 could be configured to collect and store dataon the time evolution of the heart rate, the breathing pattern, etc,e.g. for subsequent trending or statistical analysis.

The surveillance device 25 may be configured to monitor the integrity ofthe fluid connection between the patient and the extracorporeal circuit20, in particular the venous-side fluid connection (via access device14). This could be done by monitoring the presence of a signal componentoriginating from, e.g., the patient's heart or breathing system in themonitoring signal. Absence of such a signal component may be taken as anindication of a failure in the integrity of the fluid connection C, andcould bring the device 25 to activate an alarm and/or stop the bloodflow, e.g. by stopping the blood pump 3 and activating a clamping device13 on the tube segment 12. For monitoring the integrity of thevenous-side fluid connection, also known as VNM (Venous NeedleMonitoring), the surveillance device 25 may be configured to generatethe monitoring signal based on a pressure signal from the venous sensor4 a. The device 25 may also be connected to pressure sensors 4 b, 4 c,as well as any additional pressure sensors included in theextracorporeal circuit 20.

The extracorporeal circuit 20 may have the option to operate in ahemodiafiltration mode (HDF mode), in which the control unit 23activates a second pumping device (HDF pump, not shown) to supply aninfusion solution into the blood line upstream and/or downstream of thedialyser 6, e.g. into one or more of tube segments 2, 5, 10 or 12.

Obtaining the Predicted Signal Profile of First Pulses

This section describes different embodiments for predicting orestimating the signal profile of first pulses in the system shown inFIG. 4. The predicted signal profile is typically given as a series ofpressure values over a period of time normally corresponding to at leastone complete pump cycle of the blood pump 3.

FIG. 6 illustrates an example of a predicted signal profile for thesystem in FIG. 4. Since the blood pump 3 is a peristaltic pump, in whichtwo rollers 3 b engage a tube segment during a full revolution of therotor 3 a, the pressure profile consists of two pump strokes. The pumpstrokes may result in different pressure values (pressure profiles),e.g. due to slight differences in the engagement between the rollers 3 band the tube segment, and thus it may be desirable for the predictedsignal profile to represent both pump strokes. If a lower accuracy ofthe predicted signal profile can be tolerated, i.e. if the output of thesubsequent removal process is acceptable, the predicted signal profilemight represent one pump stroke only.

On a general level, the predicted signal profile may be obtained in areference measurement, through mathematical simulation of the fluidsystem, or combinations thereof.

Reference Measurement

A first main group of methods for obtaining the predicted signal profileis based on deriving a time-dependent reference pressure signal(“reference signal”) from a pressure sensor in the system, typically(but not necessarily) from the same pressure sensor that provides themeasurement signal (pressure signal) that is to be processed for removalof first pulses. During this reference measurement, the second pulsesare prevented from reaching the relevant pressure sensor, either byshutting down/deactivating the second pulse generator 3′ or by isolatingthe pressure sensor from the second pulses. In the system of FIG. 4, thereference measurement could be carried out during a priming phase, inwhich the extracorporeal circuit 20 is detached from the patient and apriming fluid is pumped through the blood lines. Alternatively, thereference measurement could be carried in a simulated treatment withblood or any other fluid. Optionally, the reference measurement couldinvolve averaging a plurality of pressure profiles to reduce noise. Forexample, a plurality of relevant signal segments may be identified inthe reference signal, whereupon these segments are aligned to achieve aproper overlap of the pressure profiles in the different segments andthen added together. The identifying of relevant signal segments may beat least partially based on timing information which indicates theexpected position of each first pulse in the reference signal. Thetiming information may be obtained from a trigger point in the outputsignal of the pump sensor 26, in a control signal of the control unit23, or in the pressure signal from another one of the pressure sensors 4a-4 c. For example, a predicted time point of a first pulse in thereference signal can be calculated based on a known difference inarrival time between the trigger point and the pressure sensor thatgenerates the reference signal. In variant, if the reference signal isperiodic, relevant signal segments may be identified by identifyingcrossing points of the reference signal with a given signal level,wherein the relevant signal segments are identified to extend betweenany respective pairs of crossing points.

In a first embodiment, the predicted signal profile is directly obtainedin a reference measurement before the extracorporeal circuit 20 isconnected to the patient, and is then used as input to the subsequentremoval process, which is executed when the extracorporeal circuit 20 isconnected to the patient. In this embodiment, it is thus assumed thatthe predicted signal profile is representative of the first pulses whenthe system is connected to the patient. Suitably, the same pumpfrequency/speed is used during the reference measurement and during theremoval process. It is also desirable that other relevant systemparameters are maintained essentially constant.

FIG. 7 is a flow chart of a second embodiment. In the second embodiment,a reference library or database is first created based on the referencemeasurement (step 701). The resulting reference library is typicallystored in a memory unit, e.g. RAM, ROM, EPROM, HDD, Flash, etc (cf. 25 bin FIG. 1) of the surveillance device (cf. 25 in FIG. 1). During thereference measurement, reference pressure signals are acquired for anumber of different operational states of the extracorporeal circuit.Each operational state is represented by a unique combination of systemparameter values. For each operational state, a reference profile isgenerated to represent the signal profile of the first pulses. Thereference profiles together with associated system parameter values arethen stored in the reference library, which is implemented as asearchable data structure, such as a list, look-up table, search tree,etc.

During the actual monitoring process, i.e. when first pulses are to beeliminated from the measurement signal, current state informationindicating the current operational state of the fluid containing systemis obtained from the system, e.g. from a sensor, a control unit orotherwise (step 702). The current state information may include acurrent value of one or more system parameters. The current value isthen matched against the system parameter values in the referencelibrary. Based on the matching, one or more reference profiles areselected (step 703) and used for preparing the predicted signal profile(step 704).

Generally, the aforesaid system parameters represent the overall systemstate, including but not limited to the structure, settings, status andvariables of the fluid containing system or its components. In thesystem of FIG. 4, exemplary system parameters may include:

-   -   Pump-related parameters: number of active pumps connected        directly or indirectly (e.g. in a fluid preparation system for        the dialyser) to the extracorporeal circuit, type of pumps used        (roller pump, membrane pump, etc), flow rate, revolution speed        of pumps, shaft position of pump actuator (e.g. angular or        linear position), etc    -   Dialysis machine settings: temperature, ultrafiltration rate,        mode changes, valve position/changes, etc    -   Disposable dialysis equipment/material: information on pump        chamber/pump segment (material, geometry and wear status), type        of blood line (material and geometry), type of dialyser, type        and geometry of access devices, etc    -   Dialysis system variables: actual absolute pressures of the        system upstream and downstream of the blood pump, e.g. venous        pressure (from sensor 4 a), arterial pressure (from sensor 4 b)        and system pressure (from sensor 4 c), gas volumes trapped in        the flow path, blood line suspension, fluid type (e.g. blood or        dialysis fluid), etc    -   Patient status: blood access properties, blood properties such        as e.g. hematocrit, plasma protein concentration, etc

It is to be understood that any number or combination of systemparameters may be stored in the reference library and/or used as searchvariables in the reference library during the monitoring process.

In the following, the second embodiment will be further explained inrelation to a number of examples. In all of these examples, the pumprevolution frequency (“pump frequency”), or a related parameter (e.g.blood flow rate) is used to indicate the current operational state ofthe fluid containing system during the monitoring process. In otherwords, the pump frequency is used as search variable in the referencelibrary. The pump frequency may e.g. be given by a set value for theblood flow rate output from the control unit, or by an output signal ofa sensor that indicates the frequency of the pump (cf. pump sensor 26 inFIG. 4). Alternatively, the pump frequency could be obtained byfrequency analysis of the pressure signal from any of the sensors 4 a-4c during operation of the fluid system. Such frequency analysis could beachieved by applying any form of harmonics analysis to the pressuresignal, such as Fourier or wavelet analysis. As indicated in FIG. 5( b),the base frequency f₀ of the pump can be identified in a resulting powerspectrum.

In a first example, the reference library is searched for retrieval ofthe reference profile that is associated with the pump frequency thatlies closest to the current pump frequency. If no exact match is foundto the current pump frequency, an extrapolation process is executed togenerate the predicted signal profile. In the extrapolation process, theretrieved reference profile is scaled in time to the current pump cycle,based on the known difference (“pump frequency difference”) between thecurrent pump frequency and the pump frequency associated with theretrieved reference profile. The amplitude scale may also be adjusted tocompensate for amplitude changes due to pump frequency, e.g. based on aknown function of amplitude as a function of pump frequency. FIG. 8illustrates a reference profile r₁(n) obtained at a flow rate of 470ml/min, and predicted signal profile u(n) which is obtained by scalingthe reference profile to a flow rate of 480 ml/min. For comparison only,a reference profile r_(actual)(n) obtained at 480 ml/min is also shown,to illustrate that extrapolation process indeed may yield a properlypredicted signal profile.

In a second example, the reference library is again searched based oncurrent pump frequency. If no exact match is found to the current pumpfrequency, a combination process is executed to generate the predictedsignal profile. Here, the reference profiles associated with the twoclosest matching pump frequencies are retrieved and combined. Thecombination may be done by re-scaling the pump cycle time of theretrieved reference profiles to the current pump frequency and bycalculating the predicted signal profile via interpolation of there-scaled reference profiles. For example, the predicted signal profileu(n) at the current pump frequency ν may be given by:

u(n)=g(ν−ν_(i))·r _(i)(n)+(1−g(ν−ν_(i)))·r _(j)(n),

wherein r_(i)(n) and r_(j)(n) denotes the two retrieved referenceprofiles, obtained at a pump frequency ν_(i) and ν_(j), respectively,after re-scaling to the current pump frequency ν, and g is a relaxationparameter which is given as a function of the frequency difference(ν−ν_(i)), wherein ν_(i)≦ν≦ν_(j) and 0≦g≦1. The skilled person realizesthat the predicted signal profile u(n) may be generated by combiningmore than two reference profiles.

FIG. 9( a) illustrates a predicted signal profile u(n) at a current flowrate of 320 ml/min for a measurement signal obtained from the venoussensor 4 a in the system of FIG. 4. The predicted signal profile u(n)has been calculated as an average of a reference profile r₁(n) obtainedat a flow rate of 300 ml/min from the venous sensor and a referenceprofile r₂(n) obtained at a flow rate of 340 ml/min from the venoussensor. For comparison only, a reference profile r_(actual)(n) obtainedat 320 ml/min is also shown, to illustrate that the combination processindeed may yield a properly predicted signal profile. In fact, thedifferences are so small that they are only barely visible in theenlarged view of FIG. 9( b).

The first and second examples may be combined, e.g. by executing theextrapolation process of the first example if the pump frequencydifference is less than a certain limit, and otherwise executing thecombination process of the second example.

In a third embodiment, like in the second embodiment shown in FIG. 7, anumber of reference signals are acquired in the reference measurement,wherein each reference signal is obtained for a specific combination ofsystem parameter values. The reference signals are then processed forgeneration of reference spectra, which are indicative of the energy andphase angle as function of frequency. These reference spectra may e.g.be obtained by Fourier analysis, or equivalent, of the referencesignals. Corresponding energy and phase data are then stored in areference library together with the associated system parameter values(cf. step 701 in FIG. 7). The implementation of the reference librarymay be the same as in the second embodiment.

During the actual monitoring process, i.e. when first pulses are to beeliminated from the measurement signal, a current value of one or moresystem parameters is obtained from the fluid containing system (cf. step702 in FIG. 7). The current value is then matched against the systemparameter values in the reference library. Based on the matching, aspecific set of energy and phase data may be retrieved from thereference library to be used for generating the predicted signal profile(cf. step 703 in FIG. 7). Generally, the predicted signal profile isgenerated by adding sinusoids of appropriate frequency, amplitude andphase, according to the retrieved energy and phase data (cf. step 704 inFIG. 7).

Generally speaking, without limiting the present disclosure, it may beadvantageous to generate the predicted signal profile from energy andphase data when the first pulses (to be removed) contain only one or afew base frequencies (and harmonics thereof), since the predicted signalprofile can be represented by a small data set (containing energy andphase data for the base frequencies and the harmonics). One the otherhand, when the power spectrum of the first pulses is more complex, e.g.a mixture of many base frequencies, it may instead be preferable togenerate the predicted signal profile from one or more referenceprofiles.

FIG. 10( a) represents an energy spectrum of a reference signal acquiredat a flow rate of 300 ml/min in the system of FIG. 4. In this example,the reference signal essentially consists of a basic pump frequency at1.2 Hz (f₀, first harmonic) and a set of overtones of this frequency(second and further harmonics). Compared to the power spectrum of FIG.5( b), the pressure signals used for generating the graphs in FIG. 10(a)-10(d) do not contain any significant frequency component at 0.5f₀ andits harmonics. The graph in FIG. 10( a) displays the relative energydistribution, wherein the energy values have been normalized to thetotal energy for frequencies in the range of 0-10 Hz. FIG. 10( b)represents energy spectra of reference signals acquired at threedifferent flow rates in the system of FIG. 4. The energy spectra aregiven in logarithmic scale versus harmonic number (first, second, etc).As shown, an approximate linear relationship can be identified betweenthe logarithmic energy and harmonic number for the first four to fiveharmonic numbers. This indicates that each energy spectrum may berepresented by a respective exponential function. FIG. 10( c)illustrates the data of FIG. 10( b) in linear scale, wherein arespective polynomial function has been fitted to the data. As indicatedin FIGS. 10( a)-10(c), the energy spectra may be represented indifferent formats in the reference library, e.g. as a set of energyvalues associated with discrete frequency values or harmonic numbers, oras an energy function representing energy versus frequency/harmonicnumber.

FIG. 10( d) illustrates a phase angle spectrum acquired together withthe energy spectrum in FIG. 10( a), i.e. for a flow rate of 300 ml/min.The graph in FIG. 10( d) illustrates phase angle as a function offrequency, and a linear function has been fitted to the data. In analternative representation (not shown), the phase spectrum may be givenas a function of harmonic number. Like the energy spectra, the phasespectra may be represented in different formats in the referencelibrary, e.g. as a set of phase angle values associated with discretefrequency values or harmonic numbers, or as a phase functionrepresenting phase angle versus frequency/harmonic number.

From the above, it should be understood that the energy and phase datathat are stored the reference library can be used to generate thepredicted signal profile. Each energy value in the energy datacorresponds to an amplitude of a sinusoid with a given frequency (thefrequency associated with the energy value), wherein the phase value forthe given frequency indicates the proper phase angle of the sinousoid.This method of preparing the predicted signal profile by combining(typically adding) sinusoids of appropriate frequency, amplitude andphase angle allows the predicted signal profile to include all harmonicsof the pump frequency within a desired frequency range.

When a predicted signal profile is to be generated, the referencelibrary is first searched based on a current value of one or more systemparameters, such as the current pump frequency. If no exact match isfound in the reference library, a combination process may be executed togenerate the predicted signal profile. For example, the two closestmatching pump frequencies may be identified in the reference library andthe associated energy and phase data may be retrieved and combined toform the predicted signal profile. The combination may be done byinterpolating the energy data and the phase data. In the example ofFIGS. 10( a)-10(d), an interpolated energy value may be calculated foreach harmonic number, and similarly an interpolated phase value could becalculated for each harmonic number. Any type of interpolation functioncould be used, be it linear or non-linear.

In the first, second and third embodiments, the reference signals andthe measurement signals are suitably obtained from the same pressuresensor unit in the fluid containing system. Alternatively, differentpressure sensor units could be used, provided that the pressure sensorunits yield identical signal responses with respect to the first pulsesor that the signal responses can be matched using a known mathematicalrelationship.

To further improve the first, second and third embodiments, the processof generating the predicted signal profile may also involve compensatingfor other potentially relevant factors that differ between the referencemeasurement and the current operational state. These so-calledconfounding factors may comprise one or more of the system parameterslisted above, such as absolute average venous and arterial pressures,temperature, blood hematocrit/viscosity, gas volumes, etc. Thiscompensation may be done with the use of predefined compensationformulas or look-up tables.

In further variations, the second and third embodiments may be combined,e.g. in that the reference library stores not only energy and phasedata, but also reference profiles, in association with system parametervalue(s). When an exact match is found in the library, the referenceprofile is retrieved from the library and used as the predicted signalprofile, otherwise the predicted signal profile is obtained byretrieving and combining (e.g. interpolating) the energy and phase data,as in the third embodiment. In a variant, the predicted signal profileu(n) at the current pump frequency ν is obtained by:

u(n)=r _(i)(n)−r ^(f) _(i)(n)+r ^(f)(n),

wherein r_(i)(n) denotes a reference profile that is associated with theclosest matching pump frequency ν_(i) in the reference library, r^(f)_(i)(n) denotes a reference profile that is reconstructed from theenergy and phase data associated with the closest matching pumpfrequency ν_(i) in the reference library, and r^(f)(n) denotes anestimated reference profile at the current pump frequency ν. Theestimated reference profile r^(f)(n) may be obtained by applyingpredetermined functions to estimate the energy and phase data,respectively, at the current pump frequency ν based on the energy andphase data associated with the closest matching pump frequency ν_(i).With reference to FIGS. 10( b)-10(c), such a predetermined function maythus represent the change in energy data between different flow rates.Alternatively, the estimated reference profile r^(f)(n) may be obtainedby retrieving and combining (e.g. interpolating) energy and phase datafor the two closest matching pump frequencies ν_(i) and ν_(j) as in thethird embodiment.

In a further variant, the reference measurement is made during regularoperation of the fluid containing system, instead of or in addition toany reference measurements made before regular operation (e.g. duringpriming or simulated treatments with blood). Such a variant presumesthat it is possible to intermittently shut off the second pulsegenerator, or to intermittently prevent the second pulses from reachingthe relevant pressure sensor. This approach is more difficult in theextracorporeal circuit 20 of FIG. 4 if the reference signals and themeasurement signals are obtained from the one and the same pressuresensor. However, this approach can e.g. be applied if the fluid systemincludes one pressure sensor that is substantially isolated from thesecond pulses. In such a situation, the reference profile (or referencespectra) may be obtained from the isolated sensor, and used forgenerating the predicted signal profile (optionally afteradjustment/modification for differences in confounding factors), whichis then used for removing first pulses from a measurement signal thatcontains both first and second pulses. For example, the pressure signalfrom the system sensor 4 c in the circuit 20 of FIG. 4 may beessentially isolated from the second pulses that originate from thepatient, and this pressure signal may thus be used in a referencemeasurement.

As explained above, the extracorporeal circuit 20 in FIG. 4 may beswitched into a HDF mode, in which an additional HDF pump is activatedto supply an infusion liquid into the blood line of the extracorporealcircuit 20. Such a change of operating mode may cause a change in thesignal characteristics of the first pulses in the measurement signal.Thus, it may necessary to account for this change, by ensuring that thereference library includes appropriate reference data (referenceprofiles and/or energy and phase angle data) associated with thisoperational state.

Alternatively, it may be desirable to isolate the pressure pulsesoriginating from the HDF pump. This could be achieved by obtaining areference profile from the pressure signal of the arterial sensor 4 b(FIG. 4). The arterial pressure signal includes pressure pulsesoriginating from the patient and from the blood pump 3, whereas pressurepulses originating from the HDF pump are significantly damped by thepatient and the blood pump 3, respectively, and thus barely reach thearterial sensor 4 b. On the other hand, the pressure signals of thevenous sensor 4 a and the system sensor 4 c contain pressure pulsesoriginating from both the patient, the blood pump 3 and the HDF pump.Thus, the arterial pressure signal may be used for obtaining thepredicted signal profile of the combined pressure pulses originatingfrom the blood pump 3 and the patient as they should look in thepressure signal from the venous sensor 4 a or the system sensor 4 c. Thepredicted signal profile may then be used for isolating the pressurepulses originating from the HDF pump in the pressure signal from thevenous sensor 4 a or the system sensor 4 c. In this example, the patientand the extracorporeal circuit 20 could be regarded as a firstsub-system (S1 in FIG. 1) and the HDF pump and the associated infusiontubing could be regarded as a second sub-system (S2 in FIG. 1), whichare connected via a fluid connection. Thus, in this example, theinventive data processing is not applied to isolate pulses originatingfrom a cyclic physiological phenomenon in the patient, but pulsesoriginating from another pump in the fluid system. It should be realizedthat in other arrangements, the reference profile may be obtained fromthe pressure signal of the venous sensor 4 a (FIG. 4), and used forprocessing the pressure signal of the arterial sensor 4 b or systemsensor 4 c.

Simulations

As an alternative to the use of reference measurements, the predictedsignal profile may be obtained directly through simulations, i.e.calculations using a mathematical model of the fluid containing system,based on current state information indicating the current operationalstate of the system. Such current state information may include acurrent value of one or more of the above-mentioned system parameters.The model may be based on known physical relationships of the systemcomponents (or via an equivalent representation, e.g. by representingthe system as an electrical circuit with fluid flow and pressure beinggiven by electrical current and voltage, respectively). The model may beexpressed, implicitly or explicitly, in analytical terms. Alternatively,a numerical model may be used. The model could be anything from acomplete physical description of the system to a simple function. In oneexample, such a simple function could convert data on the instantaneousangular velocity of the pump rotor 3 a to a predicted signal profile,using empirical or theoretical data. Such data on the instantaneousangular velocity might be obtained from the pump sensor 26 in FIG. 4.

In another embodiment, simulations are used to generate referenceprofiles for different operational states of the system. These referenceprofiles may then be stored in a reference library, which may beaccessed and used in the same way as described above for the second andthird embodiments. It is also to be understood that reference profiles(and/or corresponding energy and phase angle data) obtained bysimulations may be stored together with reference profiles (and/orcorresponding energy and phase angle data) obtained by referencemeasurement.

Removal of First Pulses

There are several different ways of removing one or more first pulsesfrom the measurement signal, using the predicted signal profile. Here,two different removal processes will be described: Single Subtractionand Adaptive Filtering. Of course, the description of removal processesand their implementations is not comprehensive (neither of the differentalternatives nor of the implementations), which is obvious to a personskilled in the art.

Depending on implementation, the predicted signal profile may be inputto the removal process as is, or the predicted signal profile may beduplicated to construct an input signal of suitable length for theremoval process.

Single Subtraction

In this removal process, a single predicted signal profile is subtractedfrom the measurement signal. The predicted signal profile may be shiftedand scaled in time and scaled in amplitude in any way, e.g. to minimizethe error of the removal. Different minimization criterions may be usedfor such an auto-scaling, e.g., minimizing the sum of the squarederrors, or the sum of the absolute errors. Alternatively oradditionally, the predicted signal profile is shifted in time based ontiming information that indicates the expected timing of the firstpulse(s) in the measurement signal. The timing information may beobtained in the same way as described above in relation to the averagingof pressure segments in the reference signal.

One potential limitation of this removal process is that therelationship between different frequencies in the predicted signalprofile is always the same, since the process only shifts and scales thepredicted signal profile. Thus, it is not possible to change therelationship between different harmonic frequencies, neither is itpossible to use only some of the frequency content in the predictedsignal profile and to suppress other frequencies. To overcome thislimitation, adaptive filtering may be used since it uses a linear filterbefore subtraction, e.g. as described in the following.

Adaptive Filtering

FIG. 11 is a schematic overview of an adaptive filter 30 and an adaptivefilter structure which is designed to receive the predicted signalprofile u(n) and a measurement signal d(n), and to output an errorsignal e(n) which forms the aforesaid monitoring signal in which thefirst pulses are removed.

Adaptive filters are well-known electronic filters (digital or analog)that self-adjust their transfer function according to an optimizingalgorithm. Specifically, the adaptive filter 30 includes a variablefilter 32, typically a finite impulse response (FIR) filter of length Mwith filter coefficients w(n).

Even if adaptive filters are known in the art, they are not readilyapplicable to cancel the first pulses in the measurement signal d(n). Inthe illustrated embodiment, this has been achieved by inputting thepredicted signal profile u(n) to the variable filter 32, which processesthe predicted signal profile u(n) to generate an estimated measurementsignal {circumflex over (d)}(n), and to an adaptive update algorithm 34,which calculates the filter coefficients of the variable filter 32 basedon the predicted signal profile u(n) and the error signal e(n). Theerror signal e(n) is given by the difference between the measurementsignal d(n) and the estimated measurement signal {circumflex over(d)}(n).

Basically, the adaptive filtering also involves a subtraction of thepredicted signal profile u(n) from the measurement signal d(n), sinceeach of the filter coefficients operates to shift and possibly re-scalethe amplitude of the predicted signal profile u(n). The estimatedmeasurement signal {circumflex over (d)}(n), which is subtracted fromthe measurement signal d(n) to generate the error signal e(n), is thusformed as a linear combination of M shifted predicted signal profilesu(n), i.e. a linear filtering of u(n).

The adaptive update algorithm 34 may be implemented in many differentways, some of which will be described below. The disclosure is in no waylimited to these examples, and the skilled person should have nodifficulty of finding further alternatives based on the followingdescription.

There are two main approaches to adaptive filtering: stochastic anddeterministic. The difference lies in the minimization of the errorsignal e(n) by the update algorithm 34, where different minimizationcriteria are obtained whether e(n) is assumed to be stochastic ordeterministic. A stochastic approach typically uses a cost function Jwith an expectation in the minimization criterion, while a deterministicapproach typically uses a mean. The squared error signal e²(n) istypically used in a cost function when minimizing e(n), since thisresults in one global minimum. In some situations, the absolute error|e(n)| may be used in the minimization, as well as different forms ofconstrained minimizations. Of course, any form of the error signal maybe used, however convergence towards a global minimum is not alwaysguaranteed and the minimization may not always be solvable.

In a stochastic description of the signal, the cost function maytypically be according to,

J(n)=E{|e(n)|²},

and in a deterministic description of the signal the cost function maytypically be according to,

J(n)=Σe ²(n).

The first pulses will be removed from the measurement signal d(n) whenthe error signal e(n) (cost function J(n)) is minimized. Thus, the errorsignal e(n) will be cleaned from first pulses while retaining the secondpulses, once the adaptive filter 30 has converged and reached theminimum error.

In order to obtain the optimal filter coefficients w(n) for the variablefilter 32, the cost function J needs to be minimized with respect to thefilter coefficients w(n). This may be achieved with the cost functiongradient vector ∇J, which is the derivative of J with respect to thedifferent filter coefficients w₀, w₁, . . . , w_(M−1). Steepest Descentis a recursive method (not an adaptive filter) for obtaining the optimalfilter coefficients that minimize the cost function J. The recursivemethod is started by giving the filter coefficients an initial value,which is often set to zero, i.e., w(0)=0. The filter coefficients isthen updated according to,

${{w\left( {n + 1} \right)} = {{w(n)} + {\frac{1}{2}{\mu \left\lbrack {- {\nabla{J(n)}}} \right\rbrack}}}},$

where w is given by,

w=[w ₀ w ₁ . . . w _(M−1)]^(T) M×1.

Furthermore, the gradient vector ∇J points in the direction in which thecost is growing the fastest. Thus, the filter coefficients are correctedin the direction opposite to the gradient, where the length of thecorrection is influenced through the step size parameter μ. There isalways a risk for the Steepest Descent algorithm to diverge, since thealgorithm contains a feedback. This sets boundaries on the step sizeparameter μ in order to ensure convergence. It may be shown that thestability criterion for the Steepest Descent algorithm is given by,

$0 < \mu < \frac{2}{\lambda_{\max}}$

where λ_(max) is the largest eigenvalue of R, the correlation matrix ofthe predicted signal profile u(n), given by

$R = {E\left\lbrack {{{{\overset{\_}{u}(n)}{{\overset{\_}{u}}^{T}(n)}} = \begin{bmatrix}{r(0)} & {r(1)} & \ldots & {r\left( {M - 1} \right)} \\{r(1)} & {r(0)} & \; & {r\left( {M - 2} \right)} \\\vdots & \vdots & \ddots & \vdots \\{r\left( {M - 1} \right)} & {r\left( {M - 2} \right)} & \ldots & {r(0)}\end{bmatrix}},} \right.}$

where ū(n) is given by,

ū(n)=[u(n)u(n−1) . . . u(n−M+1)]^(T) M×1.

If the mean squared error (MSE) cost function (defined by J=E{|e(n)|²})is used, it may be shown that the filter coefficients are updatedaccording to,

w(n+1)=w(n)+μE[ū(n)e(n)],

where e(n) is given by,

e(n)=d(n)−ū ^(T)(n)w(n).

The Steepest Descent algorithm is a recursive algorithm for calculationof the optimal filter coefficients when the statistics of the signalsare known. However, this information is often unknown. The Least MeanSquares (LMS) algorithm is a method that is based on the same principlesas the Steepest Descent algorithm, but where the statistics is estimatedcontinuously. Thus, the LMS algorithm is an adaptive filter, since thealgorithm can adapt to changes in the signal statistics (due tocontinuous statistic estimations), although the gradient may becomenoisy. Because of the noise in the gradient, the LMS algorithm isunlikely to reach the minimum error J_(min), which the Steepest Descentalgorithm does. Instantaneous estimates of the expectation are used inthe LMS algorithm, i.e., the expectation is removed. Thus, for the LMSalgorithm, the update equation of the filter coefficients becomes

w(n+1)=w(n)+μū(n)e(n).

The convergence criterion of the LMS algorithm is the same as for theSteepest Descent algorithm. In the LMS algorithm, the step size isproportional to the predicted signal profile u(n), i.e., the gradientnoise is amplified when the predicted signal profile is strong. Onesolution to this problem is to normalize the update of the filtercoefficients with

∥ū(n)∥² =ū ^(T)(n)u(n).

The new update equation of the filter coefficients is called theNormalized LMS, and is given by

${{w\left( {n + 1} \right)} = {{w(n)} + {\frac{\overset{\sim}{\mu}}{a + {{\overset{\_}{u}(n)}}^{2}}{\overset{\_}{u}(n)}{(n)}}}},$

where 0<{tilde over (μ)}<2, and a is a positive protection constant.

There are many more different alternatives to the LMS algorithm, wherethe step size is modified. One of them is to use a variable adaptationstep,

w(n+1)=w(n)+α(n)ū(n)e(n),

where α(n) for example may be,

${{\alpha (n)} = \frac{1}{n + c}},$

where c is a positive constant. It is also possible to chooseindependent adaptation steps for each filter coefficient in the LMSalgorithm, e.g., according to,

w(n+1)=w(n)+Aū(n)e(n),

where A is given by,

$A = {\begin{bmatrix}\alpha_{1} & 0 & 0 & \ldots & 0 \\0 & \alpha_{2} & 0 & \ldots & 0 \\0 & 0 & \alpha_{3} & \ldots & 0 \\\vdots & \vdots & \vdots & \ddots & \vdots \\0 & 0 & 0 & \ldots & \alpha_{M}\end{bmatrix}.}$

If instead the following cost function

J(n)=E{|e(n)|}

is used, then the update equation becomes

w(n+1)=w(n)+α sign[e(n)]ū(n).

This adaptive filter is called the Sign LMS, which is used inapplications with extremely high requirements on low computationalcomplexity.

Another adaptive filter is the Leaky LMS, which uses a constrainedminimization with the following cost function

J(n)=E{|e(n)|² }+α∥w(n)∥².

This constraint has the same effect as if white noise with variance αwas added to the predicted signal profile u(n). As a result, theuncertainty in the input signal u(n) is increased, which tends to holdthe filter coefficients back. The Leaky LMS is preferably used when R,the correlation matrix of u(n), has one or more eigenvalues equal tozero. However, in systems without noise, the Leaky LMS makes performancepoorer. The update equation of the filter coefficients for the Leaky LMSis given by,

w(n+1)=(1−μα)w(n)+μū(n)e(n).

Instead of minimizing the MSE cost function as above, the RecursiveLeast Squares (RLS) adaptive filter algorithm minimizes the followingcost function

${{J(n)} = {\sum\limits_{i = 1}^{n}{\lambda^{n - i}{{()}}^{2}}}},$

where λ is called forgetting factor, 0<λ≦1, and the method is calledExponentially Weighted Least Squares. It may be shown that the updateequations of the filter coefficients for the RLS algorithm are, afterthe following initialization

w(0)=0_(M×1)

P(0)=δ⁻¹ I _(M×M)

where I_(M×M) is the identity matrix M×M, given according to

${k(n)} = \frac{\lambda^{- 1}{P\left( {n - 1} \right)}{\overset{\_}{u}(n)}}{1 + {\lambda^{- 1}{{\overset{\_}{u}}^{T}(n)}{P\left( {n - 1} \right)}{\overset{\_}{u}(n)}}}$ξ(n)=d(n)−w ^(T)(n−1)ū(n)

w(n)=w(n−1)+k(n)ξ(n)

P(n)=λ⁻¹ P(n−1)−λ⁻¹ k(n)ū ^(T)(n)P(n−1),

where δ is a small positive constant for high signal-to-noise ratio(SNR), and a large positive constant for low SNR, δ<<0.01σ_(u) ², andξ(n) corresponds to e(n) in the preceding algorithms. During theinitialization phase the following cost function

${{J(n)} = {{\sum\limits_{i = 1}^{n}{\lambda^{n - i}{{()}}^{2}}} + {{\delta\lambda}^{n}{{w(n)}}^{2}}}},$

is minimized instead, due to the use of the initialization P(0)=δ⁻¹ I.The RLS algorithm converges in approximately 2M iterations, which isconsiderably faster than for the LMS algorithm. Another advantage isthat the convergence of the RLS algorithm is independent of theeigenvalues of R, which is not the case for the LMS algorithm.

Several RLS algorithms running in parallel may be used with different λand δ, which may be combined in order to improve performance, i.e., λ=1may also be used in the algorithm (steady state solution) with manydifferent δ:s.

It should be noted that both the LMS algorithm and the RLS algorithm canbe implemented in fixed-point arithmetic, such that they can be run on aprocessor that has no floating point unit, such as a low-cost embeddedmicroprocessor or microcontroller.

To illustrate the effectiveness of the removal process using an adaptivefilter, the top graph in FIG. 12( a) illustrates the error signal e(n)output by the adaptive filter structure in FIG. 11, using an RLSalgorithm as adaptive update algorithm 32, operating on a measurementsignal from the venous sensor 4 a in FIG. 4, at a flow rate of 430ml/min. The adaptive filter structure is provided with a predictedsignal profile obtained in a reference measurement at the same flowrate. The RLS algorithm, designed with M=15, converges after about 2M,which equals 3 seconds with the current sampling frequency of 10 Hz. Thetop graph thus shows the measurement signal after elimination of thefirst pulses. The bottom graph in FIG. 12( a) is included for reference,and shows the measurement signal from the venous sensor 4 a while theblood pump 3 is stopped. Clearly, the adaptive filtering is operable toprovide, after a convergence period, a monitoring signal that properlyrepresents the second pulses.

FIG. 12( b) corresponds to FIG. 12( a), but is obtained for ameasurement signal from the arterial sensor 4 b in FIG. 4.

Irrespective of implementation, the performance of the adaptive filter30 (FIG. 11) may be further improved by switching the adaptive filter 30to a static mode, in which the update algorithm 34 is disabled and thusthe filter coefficients of the filter 32 (FIG. 11) are locked to acurrent set of values. The switching of the adaptive filter 30 may becontrolled by an external process that analyses the second pulses in theerror signal e(n), typically in relation to first pulse data. The firstpulse data may be obtained from the measurement signal, a referencesignal (see above), a dedicated pulse sensor, a control unit for thefirst pulse generator, etc. The adaptive filter 30 may be switched intothe static mode if the external process reveals that the rate of secondpulses starts to approach the rate of the first pulses and/or that theamplitude of the second pulses is very weak (in relation to an absolutelimit, or in relation to a limit given by the amplitude of the firstpulses). The adaptive filter may remain in static mode for apredetermined time period, or until released by the process.

The invention has mainly been described above with reference to a fewembodiments. However, as readily appreciated by a person skilled in theart, other embodiments than the ones disclosed above are equallypossible with the scope and spirit of the invention, which is definedand limited only by the appended patent claims.

For example, the measurement and reference signals may originate fromany conceivable type of pressure sensor, e.g. operating by resistive,capacitive, inductive, magnetic or optical sensing, and using one ormore diaphragms, bellows, Bourdon tubes, piezo-electrical components,semiconductor components, strain gauges, resonant wires, accelerometers,etc.

Although FIG. 1 indicates that the pressure sensor 4 a-4 c is connectedto the first sub-system S1, it may instead be connected to measure thefluid pressure in the second sub-system S2. Further, the fluidcontaining system need not be partitioned into first and secondsub-systems S1, S2 connected via a fluid connection C, but could insteadbe a unitary fluid containing system associated with a first pulsegenerator and a second pulse generator, wherein the each pressure sensoris arranged in the fluid containing system to detect a first pulseoriginating from the first pulse generator and a second pulseoriginating from the second pulse generator.

Further, the inventive technique is applicable for monitoring in alltypes of extracorporeal blood flow circuits in which blood is taken fromthe systemic blood circuit of the patient to have a process applied toit before it is returned to the patient. Such blood flow circuitsinclude circuits for hemodialysis, hemofiltration, hemodiafiltration,plasmapheresis, apheresis, extracorporeal membrane oxygenation, assistedblood circulation, and extracorporeal liver support/dialysis. Theinventive technique is likewise applicable for monitoring in other typesof extracorporeal blood flow circuits, such as circuits for bloodtransfusion, infusion, as well as heart-lung-machines.

The inventive technique is also applicable to fluid systems containingother liquids than blood.

Further, the inventive technique is applicable to remove pressure pulsesoriginating from any type of pumping device, not only rotary peristalticpumps as disclosed above, but also other types of positive displacementpumps, such as linear peristaltic pumps, diaphragm pumps, as well ascentrifugal pumps. In fact, the inventive technique is applicable forremoving pressure pulses that originate from any type of pulsegenerator, be it mechanic or human.

Likewise, the inventive technique is applicable to isolate pressurepulses originating from any type of pulse generator, be it human ormechanic.

The inventive technique need not operate on real-time data, but could beused for processing off-line data, such as a previously recordedmeasurement signal.

1. A method for processing a time-dependent measurement signal obtainedfrom a pressure sensor in a fluid containing system associated with afirst pulse generator and a second pulse generator, wherein the pressuresensor is arranged in the fluid containing system to detect a firstpulse originating from the first pulse generator and a second pulseoriginating from the second pulse generator, said method comprising:receiving the time-dependent measurement signal, obtaining a first pulseprofile which is a predicted temporal signal profile of the first pulse,and filtering the time-dependent measurement signal in the time-domain,using the first pulse profile, to essentially eliminate the first pulsewhile retaining the second pulse.
 2. The method of claim 1, wherein thestep of filtering comprises subtracting the first pulse profile from thetime-dependent measurement signal.
 3. The method of claim 2, wherein thestep of subtracting comprises adjusting a phase of the first pulseprofile in relation to the measurement signal, wherein said phase isindicated by phase information obtained from a phase sensor coupled tothe first pulse generator, or from a control unit for the first pulsegenerator.
 4. The method of claim 1, wherein the first pulse profile isobtained in a reference measurement in said fluid containing system,wherein the reference measurement comprises the steps of: operating thefirst pulse generator to generate at least one first pulse, andobtaining the first pulse profile from a reference signal generated by areference pressure sensor in the fluid containing system.
 5. The methodof claim 4, wherein the first pulse generator is operated to generate asequence of first pulses during the reference measurement, and whereinthe first pulse profile is obtained by identifying and averaging a setof first pulse segments in the reference signal.
 6. The method of claim4, wherein the reference measurement is effected intermittently duringoperation of the fluid containing system to provide an updated firstpulse profile.
 7. The method of claim 4, wherein the pressure sensor isused as said reference pressure sensor.
 8. The method of claim 1,wherein the step of obtaining comprises obtaining a predetermined signalprofile.
 9. The method of claim 8, wherein the step of obtaining furthercomprises modifying the predetermined signal profile according to amathematical model based on a current value of one or more systemparameters of the fluid containing system.
 10. The method of claim 4,wherein the fluid containing system is operated, during the referencemeasurement, such that the reference signal contains a first pulse andno second pulse.
 11. The method of claim 4, wherein the referencemeasurement comprises: obtaining a combined pulse profile based on afirst reference signal containing a first pulse and a second pulse;obtaining a second pulse profile based on a second reference signalcontaining a second pulse and no first pulse, and obtaining thepredicted signal profile by subtracting the second pulse profile fromthe combined pulse profile.
 12. The method of claim 1, furthercomprising the step of obtaining a current value of one or more systemparameters of the fluid containing system, wherein the first pulseprofile is obtained as a function of the current value.
 13. The methodof claim 12, wherein said step of obtaining the first pulse profilecomprises: identifying, based on the current value, one or morereference profiles in a reference database; and obtaining the firstpulse profile based on said one or more reference profiles.
 14. Themethod of claim 13, wherein said one or more system parameters isindicative of the rate of first pulses in the fluid containing system.15. The method of claim 14, wherein the first pulse generator comprisesa pumping device and the system parameter is indicative of a pumpfrequency of the pumping device.
 16. The method of claim 13, whereineach reference profile in the reference database is obtained by areference measurement in the fluid containing system for a respectivevalue of said one or more system parameters.
 17. The method of claim 12,wherein said step of obtaining the first pulse profile comprises:identifying, based on the current value, one or more combinations ofenergy and phase angle data in a reference database; and obtaining thefirst pulse profile based on said one or more combinations of energy andphase angle data.
 18. The method of claim 17, wherein the first pulseprofile is obtained by combining a set of sinusoids of differentfrequencies, wherein the amplitude and phase angle of each sinusoid isgiven by said one or more combinations of energy and phase angle data.19. The method of claim 12, wherein said step of obtaining the firstpulse profile comprises: inputting the current value into an algorithmwhich calculates the response of the pressure sensor based on amathematical model of the fluid containing system.
 20. The method ofclaim 1, wherein the step of filtering comprises subtracting the firstpulse profile from the measurement signal, and wherein the step ofsubtracting is preceded by an adjustment step, in which at least one ofthe amplitude, the time scale, and the phase of the first pulse profileis adjusted with respect to the measurement signal.
 21. The method ofclaim 20, wherein the adjustment step comprises minimizing a differencebetween the first pulse profile and the measurement signal.
 22. Themethod of claim 1, wherein the step of filtering comprises: supplyingthe first pulse profile as input to an adaptive filter; calculating anerror signal between the measurement signal and an output signal of theadaptive filter; and providing the error signal as input to the adaptivefilter, whereby the adaptive filter is arranged to essentially eliminatethe first pulse in the error signal.
 23. The method of claim 22, whereinthe adaptive filter comprises a finite impulse response filter withfilter coefficients that operate on the first pulse profile to generatethe output signal, and an adaptive algorithm which optimizes the filtercoefficients as a function of the error signal and the first pulseprofile.
 24. The method of claim 22, further comprising the step ofcontrolling the adaptive filter to lock the filter coefficients, basedon a comparison of the rate, the amplitude, or the rate and amplitude ofthe second pulses to a limit value.
 25. The method of claim 1, whereinthe fluid containing system comprises an extracorporeal blood flowcircuit for connection to a blood system in a human body, and whereinthe first pulse generator comprises a pumping device in theextracorporeal blood flow circuit, and wherein the second pulsegenerator comprises a physiological pulse generator in the human body.26. The method of claim 25, wherein the second pulse generator is atleast one of a heart, a breathing system, and a vasomotor affected by anautonomic nervous system.
 27. The method of claim 25, wherein theextracorporeal blood flow circuit comprises an arterial access device, ablood processing device, and a venous access device, wherein the humanblood system comprises a blood vessel access, wherein the arterialaccess device is configured to be connected to the human blood system,wherein the venous access device is configured to be connected to theblood vessel access to form a fluid connection, and wherein the firstpulse generator comprises a pumping device arranged in theextracorporeal blood flow circuit to pump blood from the arterial accessdevice through the blood processing device to the venous access device,said method further comprising the step of receiving the measurementsignal either from a venous pressure sensor located downstream of thepumping device, or from an arterial pressure sensor located upstream ofthe pumping device.
 28. A computer program product comprisinginstructions for causing a computer to perform the method of claim 1.29. A device for processing a time-dependent measurement signal obtainedfrom a pressure sensor in a fluid containing system associated with afirst pulse generator and a second pulse generator, wherein the pressuresensor is arranged in the fluid containing system to detect a firstpulse originating from the first pulse generator and a second pulseoriginating from the second pulse generator, said device comprising: aninput for the measurement signal, a signal processor connected to saidinput and comprising a processing module configured to obtain a firstpulse profile which is a predicted temporal signal profile of the firstpulse, and to filter the measurement signal in a time-domain, using thefirst pulse profile, to essentially eliminate the first pulse whileretaining the second pulse.
 30. A device for processing a time-dependentmeasurement signal obtained from a pressure sensor in a fluid containingsystem associated with a first pulse generator and a second pulsegenerator, wherein the pressure sensor is arranged in the fluidcontaining system to detect a first pulse originating from the firstpulse generator and a second pulse originating from the second pulsegenerator, said device comprising: means for receiving the measurementsignal, means for obtaining a first pulse profile which is a predictedtemporal signal profile of the first pulse, and means for filtering thetime-dependent measurement signal in a time-domain, using the firstpulse profile, to essentially eliminate the first pulse while retainingthe second pulse.
 31. A method for processing a time-dependentmeasurement signal obtained from a pressure sensor in a fluid containingsystem associated with a first pulse generator and a second pulsegenerator, wherein the pressure sensor is arranged in the fluidcontaining system to detect a first pulse originating from the firstpulse generator and a second pulse originating from the second pulsegenerator, said method comprising: receiving the measurement signal,obtaining a standard signal profile of the first pulse, and subtractingthe standard signal profile from the time-dependent measurement signalin a time-domain, wherein the standard signal profile has such anamplitude and phase that the first pulse is essentially eliminated andthe second pulse is retained.
 32. A device for processing atime-dependent measurement signal obtained from a pressure sensor in afluid containing system associated with a first pulse generator and asecond pulse generator, wherein the pressure sensor is arranged in thefluid containing system to detect a first pulse originating from thefirst pulse generator and a second pulse originating from the secondpulse generator, said device comprising: an input for the measurementsignal, a signal processor connected to said input and comprising aprocessing module configured to obtain a standard signal profile of thefirst pulse, and to subtract the standard signal profile from thetime-dependent measurement signal in a time-domain, wherein the standardsignal profile has such an amplitude and phase that the first pulse isessentially eliminated and the second pulse is retained.